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A new definition of dissipativity for neural networks is presented in this paper.By constructing proper Lyapunov func-tionals and using some analytic techniques,sufficient conditions are given to ensure the dissipativity of neural networks with or without time-varying parametric uncertainties and the integro-differential neural networks in terms of linear matrix inequalities.Numerical examples are given to illustrate the effectiveness of the obtained results.
A new definition of dissipativity for neural networks is presented in this paper. By Article: Building Lyapunov func- tionals and using some analytic techniques, sufficient conditions are given to ensure the dissipativity of neural networks with or without time-varying parametric uncertainties and the integro- differential neural networks in terms of linear matrix inequalities. Numerical examples are given to illustrate the effectiveness of the obtained results.